(3.92.96.236) 您好!臺灣時間:2021/05/07 15:39
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

: 
twitterline
研究生:簡建興
研究生(外文):Jiang-Shing Jiang
論文名稱:視覺化工具於腦訊號分析之研究
論文名稱(外文):A Visualization Map for Brain Signal Analysis
指導教授:陳德懷陳德懷引用關係蘇木春蘇木春引用關係
指導教授(外文):Tak-Wai ChanMu-Chun Su
學位類別:碩士
校院名稱:國立中央大學
系所名稱:網路學習科技研究所
學門:教育學門
學類:教育科技學類
論文種類:學術論文
論文出版年:2005
畢業學年度:93
語文別:中文
論文頁數:90
中文關鍵詞:視覺化工具類神經網路功能性核磁共振影像事件相關電位
外文關鍵詞:visualization mapfMRIneural networksERP
相關次數:
  • 被引用被引用:1
  • 點閱點閱:264
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:48
  • 收藏至我的研究室書目清單書目收藏:5
為了研究人類最後的黑盒子“大腦”,相關的研究工具相繼推出以幫助人們解答對大腦的疑問,當中以功能性核磁共振儀和腦電波儀的應用最為廣泛,功能性核磁共振影像具有良好的空間解析度,不僅應用到醫療上幫助我們了解大腦的組織,更提供我們以認知實驗方式來對大腦功能的定位,而腦電波圖的良好時間解析度,也幫助我們了解大腦真正的活動-神經元反應,及大腦處理認知歷程的時間性,但由於這些工具所得到信號都是極為微弱且特性複雜,使得我們在處理這些訊號上需要一個良好的方法來幫助得正確且精準的大腦反應。
為了偵測功能性核磁共振影像的活化區域和腦電波的來源定位,我們提出一個視覺化工具(V map),藉著視覺的檢查視覺化工具所產生的影像,使用者能很容易定義出功能性核磁共振影像的活化區域和腦電波的來源位置,所提出的視覺化工具完全利用活化像素的空間連結特性,這是一個重要的因素在決定大腦的活動,這個方法的優點是不需要一個先備的知識,我們使用一些資料集來証明這個方法的應用性,並利用ROC(receiver operating characteristic)分析,來探討視覺化工具及其他方法運用在功能性影像分析的效能特性,以及在偵測未知之功能性反應的精確性。結果顯示,在適當的信號變化對雜訊比(contrast-to-noise ratio)之下,視覺化工具確實能有效地偵測出功能性核磁共振影像活化區域和腦電波的來源。
在功能性核磁共振影像(fMRI)中,我們藉由模擬信號和實際人體實驗的資料來証實V map的強健性和有效,比較到以模式為主的方法,V map能有效的偵測fMRI的活化區域而不需要一個預期血液動力反應的先備知識,比較到資料導向為主的方法,V map提供幾個有魅力的特色:1)處理時間(含分群演算法)不需要很長;2)決定最佳的分群數量已不再是問題;3)資料的順序和群聚中心的隨機初始不再是問題;4)使用者不需要去確定真正符合活化區域的群聚。
在腦電波(EEG)中,我們也利用模擬信號和實際人體實驗的資料來証實V map的堅實和有效,比較到偶極子為主的方法,V map能有效的偵測EEG的來源位置而不需要假設任何的偶極子來源數量,比較到無母數統計方法(statistical nonparametric mapping method),V map提供幾個有魅力的特色:1)處理時間不需要很長;2)單一樣本資料就可檢測來源定位;3)樣本數不會影響結果的信賴度;4) V map對雜訊的影響度低。
另外,V map有能力偵測區域中像素具有類似活化的參數,但可能不同於一個真正模式函數,假如這些未預期的活化區域能更進一步檢閱,那麼會有更多資訊被顯露出來。
For opening the black box of human, understanding the brain, many tools have been applied to help people probe the questions of brain. Functional magnetic resonance imaging (fMRI) and Electroencephalography (EEG) are of the most extensive application. fMRI has very high spatial resolution which not only helps us understand tissue of cortex, but also provides us information of the functional sites of brain in cognitive experiment. On the other hand, EEG has very high temporal resolution; it helps us to enable us to see the direct action of brain (neurons) and the sequential processing of brain on cognitive operation. However, the neural signals are very weak and usually present complex property in these tools. We need a better method to get correct and precise brain action on processing singles.
We propose a visualization map for the detection of activated regions in fMRI and of the located source in EEG. Via the visual examination of the map, the user can easily identify activated regions in the fMRI and the located source in EEG. The proposed visualization map fully utilizes the property of the spatial connectivity of activated pixels which is a key factor on determining the significance of activated regions in cortex. The advantage of this technique is people can identify regions of activation without any priori knowledge. Several datasets have also been employed to verify the broad applicability of the technique. Receiver operating characteristic (ROC) analysis has been used to evaluate the performance under many conditions. Results show that with certain contrast-to-noise ratios, visualization map can detect the functional activation in fMRI and source location in EEG.
In fMRI, the robustness and effectiveness of the V map are demonstrated by the simulation results on an artificial data set and a real life one. Comparing to model-based methods, our V map can effectively detect regions of activation in fMRI without a priori knowledge of expected hemodynamic responses. Comparing with data-driven-based methods, the V map offers several appealing properties: 1) the processing time involved with clustering algorithms is no longer needed; 2) the determination of the optimal cluster number is not necessary; 3) the order of data and the random initialization of the cluster centers are not necessary; 4) the user no longer needs to determine which cluster really corresponds to the activated regions.
In EEG, we also use an artificial data set and a real life one to demonstrate the robustness and effectiveness of the V map. Comparing to dipole-model method, V map can efficiently detect location of sources in EEG without a priori assumption on the number of dipoles in the brain. Comparing with statistical nonparametric mapping method, V map offers several appealing properties: 1) the processing time is no longer needed; 2) one condition is sufficient to define sources location; 3) the number of samples does not influence the reliability of result; 4) correcting ratio of V map is very low in the different SNR.
In addition, the V map is capable of detecting regions within which pixels have similar activation patterns which may be different to the real model function. If these un-expected activation regions can be further inspected more information may be revealed.
摘要 I
Abstract III
致謝 VI
目錄 VII
表目錄 X
圖目錄 XI
第一章 緒論 1
1.1 研究動機 1
1.2 研究範圍 2
1.2.1 fMRI 2
1.2.2 EEG 5
1.3 研究目的 7
第二章 研究方法 8
2.1 前處理 10
2.2 轉換 10
2.3 切割 11
第三章 V map方法於功能性磁振造影影像分析之應用 13
3.1 文獻探討 13
3.1.1模式分析功能性磁振造影影像的檢測方法 14
3.1.2資料導向分析功能性磁振造影影的群聚分析方法 18
3.2 實驗材料與實驗方法 20
3.2.1實驗方法 20
3.2.2績效衡量─ROC曲線 22
3.2.3功能性磁振造影影像-模擬刺激訊號 26
3.2.4功能性磁振造影影像-實際動作刺激 27
3.3 實驗結果 28
3.3.1功能性磁振造影影像-模擬刺激訊號之實驗結果 28
3.3.2功能性磁振造影影像-實際動作刺激之實驗結果 30
3.4 結論與討論 32
3.4.1結論 32
3.4.2討論 32
第四章 V map方法於事件相關電位之來源定位分析之應用 33
4.1 文獻探討 33
4.1.1分離式事件相關電位來源定位分析檢測方法 34
4.1.2分佈式事件相關電位來源定位分析檢測方法 35
4.2 實驗材料與實驗方法 37
4.2.1頭部模型 37
4.2.2實驗方法 38
4.2.3事件相關電位-模擬刺激訊號 40
4.2.4事件相關電位-實際動作刺激 42
4.3 實驗結果 44
4.3.1事件相關電位-模擬刺激訊號之實驗結果 44
4.3.2事件相關電位-實際動作刺激之實驗結果 49
4.4 結論與討論 51
4.4.1結論 51
4.4.2討論 52
第五章 綜合討論與未來研究 53
5.1 綜合討論 53
5.1.1 fMRI 53
5.1.2 EEG 54
5.2 未來研究 55
參考文獻 57
附錄A 自動閥值演算法 68
附錄B 形態學運算(Morphological operations) 71
附錄C The low resolution brain electromagnetic tomography 73
(LORETA)方法 73
[1]J. B. Anthony, and J. S. Terrence. “An information-maximization approach to blind separation and blind deconvolution”, Neural Computation,Vol. 7, No. 6, pp. 1129-1159, 1995.
[2]J. P. Ary, S. A. Klein, and D. H. Fender. “Location of sources of evoked scalp potentials: corrections of skull and scalp thickness”, IEEE Trans Biomed Eng,;Vol.28, pp. 447-52, 1981.
[3]W. Backfrieder, R. Baumgartner, M. Samal, E. Moser and H. Bergmann. “Quantification of Intensity Variations in Functional MR Images Using Rotated Principal Components”, Phys. Med. Bio,Vol. 41, No. pp. 1425-1438, 1996.
[4]J. R. Baker, R. M. Weisskoff, and C. E. Stern. “Statistical Assessment of Functional MRI Signal Change”, Proc. SMR 2nd Ann. Meeting, San Francisco, USA, 1994.
[5]P. A. Bandettini, E. C. Wong, R. S. Hinks, R. S. Tikofsky, and J. S. Hyde. “Time Course EPI of Human Brain Function during Task Activation”, Magn. Reson. Med,Vol. 25, pp. 390-397, 1992.
[6]P. A. Bandettini, A. Jesmanowicz, E. C. Wong, and J. S. Hyde. “Processing Strategies for Time-Course Data Sets in Functional MRI of the Human Brain”, Magn. Reson. Med, Vol. 30, pp. 161-173, 1993.
[7]C. Baudelet, and B. Gallez. “Cluster analysis of BOLD fMRI time series in tumors to study the heterogeneity of hemodynamic response to treatment”, Magn Reson Med, Vol. 49, No. 6, pp. 985-990, 2003.
[8]R. Baumgertner, G. Scarth, C. Teichtmeister, R. Somorjai and E. Moser. “Fuzzy Clustering Improves Reproducibility of Gradient Echo Functional MRI by Separating Various Contributions from Venous Vessels in the Human Visual Cortex”, Proc. ISMRM 5th Ann. Meeting, Vancouver, Canada, pp.1662, 1997.
[9]R. Baumgertner, C. Windischberger, and E. Moser. “Quantification in Functional Magnetic Resonance Imaging: Fuzzy Clustering Vs. Correlation Analysis”, Magn. Reson. Imag, Vol. 16, No. 2, pp. 115-125, 1998.
[10]A. Baune, F. T. Sommer, M. Erb, D. Wildgruber, B. Kardatzki, G. Palm, and W. Grodd. “Dynamical Cluster Analysis of Cortical fMRI Activation”, NeuroImage Vol. 9, pp. 477-489, 1999.
[11]P. Berg, and M. Scherg. “A fast method for forward computation of multiple-shell spherical head models”, Electroencephalogr Clin Neurophysiol, Vol. 90, pp. 58-64, 1994.
[12]J. C. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum, New York, 1981.
[13]J. L. Boxerman, P. A. Bandettini, K. K. Kwong, J. R. Baker, T. L. Davis, B. R. Rosen, and R. M. Weisskoff. “The Intravascular Contribution to fMRI Signal Change: Monte Carlo Modeling and Diffusion-Weighted Studies in Vivo”, Magn. Reson. Med, Vol. 34, pp. 4-10, 1995.
[14]G. M. Boynton, S. A. Engel, G. H. Glover, and D. J. Heeger. “Linear Systems Analysis of Functional Magnetic Resonance Imaging in Human V1”, J. Neurosci. Vol. 16, pp. 4207-4221, 1996.
[15]R. L. Buckner, P. A. Bandettini, K. M. O’Craven, R. L. Savoy, S. E. Petersen, M. E. Raichle, and B. R. Rosen. “Detection of Corteical Activation During Averaged Single Trials of a Cognitive Task Using Functional Magnetic Resonance Imaging”, Proc. Natl. Acad. Sci, USA, Vol. 93, pp. 14878-14883, 1996.
[16]R. Cabeza, and L. Nyberg. “Imaging cognition. II. An empirical review of 275 PET and fMRI studies”, J Cogn Neurosci (Review), Vol. 12, pp. 1-47, 2000.
[17]K. H. Chuang, M. J. Chiu, C. C. Lin, and J. H. Chen. “Finding Different Physiological Signal Sources of fMRI Data Using Paradigm-Free Kohonen Clustering Network and Fuzzy C-Means”, Proc. ESMRMB 15th Ann. Meeting, Geneva, Switzerland, pp. 143-144, 1998.
[18]R. T. Constable, G. McCarthy, T. Allison, A. W. Anderson, and J. C. Gore. “Functional Brain Imaging at 1.5 T Using Conventional Gradient Echo MR Imaging Technique”, Magn. Reson. Imaging, Vol. 11, pp. 451-459, 1993.
[19]R. T. Constable, P. Skudlarski, and J. C. Gore. “An ROC Approach for Evaluating Functional Brain MR Imaging and Postprocessing Protocols”, Magn, Reson. Med, Vol. 34 pp. 57-64, 1995.
[20]R. G. de Perala, and S. G. Andino. “Comparison of algorithms for the localization of focal sources: evaluation with simulated data and analysis of experimental data”, Int J Bioelectromagn, 2002.
[21]R. G. de Perala, M. M. Murray, C. M. Michel, R. Martuzzi, and S. G. Andino. “Electrical neuroimaging based on biophysical constraints”, Neuroimage, Vol. 21, pp. 527-39, 2004a.
[22]X. Ding, J. Tkach, P. Ruggieri, T. Masaryk. “Analysis of time-course functional MRI data with clustering method without use of reference signal”, Proc., SMR, 2nd Annual Meeting, San Francisco; pp. 630, 1994.
[23]X. Ding, T. Masaryk, P. Ruggieri, and J. Tkach. “Detection of activation patterns in dynamic functional MRI with a clustering technique”, Proc., SMR, 4th Annual Meeting, New York, pp. 1798, 1996.
[24]D. Evgenia, B. Markus, W. Christian, H. Kurt, and M. Ewald. “A quantitative comparison of functional MRI cluster analysis”, Artificial Intelligence in Medicine, 2003.
[25]M. J. Fadili, S. Ruan, D. Bloyet, and B. Mazoyer. “A Multistep Unsupervised Fuzzy Clustering Analysis of fMRI Time Series”, Human Brain Mapping, Vol. 10, pp. 160–178, 2000.
[26]D. H. Fender. “Source localization of brain electrical activity”, In: Gevins AS, Remond A, editors. Handbook of eletroencephalography and clinical neurophysiology, Vol. 1, Methods of analysis of brain electrical and magnetic signals. pp. 355-99, 1987.
[27]T. Fernandez. “EEG activitation patterns during the performance of tasks involving different components of mental calculation”, Electroencephalography and Clinical Neurophysiology. Vol. 94, pp. 175-182, 1995.
[28]H. Fischer and J. Hennig. “Clustering of Functional MR Data”, Proc. ISMRM 4th Ann. Meeting, New York, USA, pp. 1779, 1996.
[29]H. Fischer, and J. Henning. “Neural Network-Based Analysis of MR Time Series”, Magn Reson Med, Vol. 41, pp. 124-131, 1999.
[30]S. D. Forman, J. D. Cohen, M. Fitzgerald, W. F. Eddy, M. A. Mintun, and D. Noll. “Improved assessment of significant activation in functional magnetic resonance imaging (fMRI): use of a cluster-size threshold”, Magn Reson Med Vol. 3, pp. 636-647, 1995.
[31]J. Frahm, K. Merboldt, and W. Hanicke. “Functional MRI of Human Brain Activation at High Spatial Resolution”, Magn. Reson. Med. Vol. 29, pp. 139-144, 1992.
[32]K. J. Friston, P. Jezzard, and R. Turner. “Analysis of Functional MRI Time-Series”, Human Brain Mapping Vol. 1, pp. 153-171, 1994.
[33]K. J. Friston, K. J. Worsley, R. S. J. Frackowiak, J. C. Mazziotta, and A. C. Evans. “Assessing the significance of focal activation using their special extent”, Hum Brain mapping Vol. 1, pp. 210-220, 1995.
[34]K. J. Friston, A. P. Holmes, K. J. Worsley, J. B. Poline, C. D. Frith, and R. S. J. Frackowiak. “Statistical Parametric Maps in Functional Imaging - A General Linear Approach”, Human Brain Mapping Vol. 2, pp. 189-210, 1995.
[35]X. Golay, S. Kollias, D. Meier, and P. Boesinger. “Optimization of a fuzzy clustering technique and comparison with conventional post processing methods in fMRI”, Proc., SMR, 4th Annual Meeting, New York, pp. 1787, 1996.
[36]X. Golay, S. Kollias, D. Meier, A. Valavanis, and P. Boesiger. “Fuzzy membership vs. probability in cross correlation based fuzzy clustering on fMRI data. In Third International Conference on Functional Mapping of the Human Brain”, NeuroImage, Vol. 3, No. 3, pp. S481, 1997.
[37]X. Golay, S. Kollias, G. Stoll, D. Meier, A. Valavanis, and P. Boesiger. “A New Correlation-Based Fuzzy Logic Clustering Algorithm for fMRI”, Magn. Reson. Med, Vol. 40, pp. 249-260, 1998.
[38]C. Goutte, P. Toft, E. Rostrup, F. A. Nielsen, and L. K. Hansen. “On Clustering fMRI Time Series”, NeuroImage, Vol. 9, pp. 298-310, 1999.
[39]I. Haalman, and E. Vaadia. “Dynamics of neuronal interactions: relation to behavior, firing rates, and distance between neurons”, Human Brain Mapping, Vol. 5, pp. 249-253, 1997.
[40]M. S. Hämäläinen, and R. J. Ilmoniemi. “Interpreting measured magnetic fields of the brain: estimates of current distributions”, Technical report TKK-F-A559, Helsinki University of Technology, Espoo; 1984.
[41]M. S. Hämäläinen, and R. J. Ilmoniemi. “Interpreting measured magnetic fields of the brain-minimum norm estimates”, Med Biol Eng Comput, Vol. 32, pp. 35-42, 1994.
[42]R. M. Haralick, S. R. Sternberg, and X. Zhuang. “Image Analysis Using Mathematical Morphology”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 9, No. 4, pp. 532-550, 1987.
[43]B. He, T. Musha, Y. Okamoto, S. Homma, Y. Nakajima, and T. Sato. “Electrical dipole tracing in the brain by means of the boundary element method and its accuracy”, IEEE Trans. Biomed. Eng, Vol. 34, pp. 406–414, 1987.
[44]H. L. F. Helmholtz. “Ueber einige Gesetze der Vertheilung elektrischer Ströme in körperlichen Leitern mit Anwendung aud die thierisch-elektrischen Versuche”, Ann Physik und Chemie Vol. 9, No. 211-233, 1853.
[45]X. Hu, T. H. Le, and K. Ugurbil. “Evaluation of the Early Response in fMRI in Individual Subjects Using Short Stimulus Duration”, Magn. Reson. Med, Vol. 37, pp. 877-884, 1997.
[46]M. Jarmasz and R. L. Somorjai. “Time to Join! Cluster-merging in Unsupervised Fuzzy Clustering of Functional MRI Data”, Proc. ISMRM 6th Ann. Meeting, Sydney, Australia, pp. 2068, 1998.
[47]R. T. Knight. “Decreased response to novel stimuli after prefrontal lesions in man”, Electroenceph clin Neurophysiol, Vol. 59, pp. 9-10, 1984.
[48]T. Kohonen. Self-Organizing Maps, Springer-Verlag, New York, pp. 31, 1995.
[49]S. Kollias, X. Golay, and D. Meier. “Blood Oxygenation Level Dependent (BOLD) Signal Response to Progressive Shortening of the Rest Period Between Constant Activated Phases at High Temporal Resolution”, Proc. ISMRM 4th Ann. Meeting, New York, USA, pp. 1758, 1996.
[50]M. Kraaijveld, A. J. Mao, and A. K. Jain. “A nonlinear projection method based on Kohonen’s topology preserving maps”, IEEE Trans. On Neural Networks, Vol. 6, pp. 548-559, 1995.
[51]G. Kruger, J. Frahm, and A. Kleinschmidt. “The Cerebral Blood Oxygenation Response to Functional Challenge: Differences between Human Motor and Visual Cortex”, Proc. ISMRM 4th Ann. Meeting, New York, USA, pp. 1757, 1996.
[52]K. K. Kwong. “Functional Magnetic Resonance Imaging with Echo Planar Imaging”, Magn. Reson. Q., Vol. 11, No. 1, pp. 1-20.
[53]K. K. Kwong, J. W. Belliveau, D. A. Chesler, I. E. Goldberg, R. M. Weisskoff, B. P. Poncelet, D. N. Kennedy, B. E. Hoppel, M. S. Cohen, R. Turner, H.-M. Chen, T. J. Brady, and B. R. Rosen. “Dynamic Magnetic Resonance Imaging of Human Brain Activity During Primary Sensory Stimulation”, Proc. Natl. Acad. Sci, USA Vol. 89, pp. 5675-5679, 1992.
[54]R. R. Llinas. “The intrinsic electrophysiological properties of mammalian neurons: insights into central nervous system function”, Science, Vol. 242, pp. 1654-1664, 1988.
[55]M. J. McKeown, T.-P. Jung, S. Makeig, G. G. Brown, S. S. Kindermann, T.-W. Lee and T. J. Sejnowski. “Spatially Independent Activity Patterns in Functional Magnetic Resonance Imaging Data During the Stroop Color-naming Task”, Proc. Natl. Acad. Sci, USA, Vol. 95, pp. 803-910, 1998.
[56]M. J. McKeown, S. Makeig, G. G. Brown, T.-P. Jung, S. S. Kindermann, A. J. Bell and T. J. Sejnowski. “Analysis of fMRI Data by Blind Separation into Independent Spatial Components”, Hum. Brain Mapp, Vol. 6, No. 3, pp. 160-88, 1998.
[57]C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli, and R. G. de Peralta. “EEG source imaging”, Clinical Neurophysiology, Vol. 115, pp. 2195-2222, 2004.
[58]J. C. Mosher, P. S. Lewis, and R. M. Leahy. “Multiple dipole modeling and Localization from spatio-temporal MEG data”, IEEE Trans Biomed Eng Vol. 39, pp. 541-557, 1992.
[59]S. C. Ngan, and X. Hu. “Analysis of Functional Magnetic Resonance Imaging Data Using Self-Organizing Mapping With Spatial Connectivity”, Magn Reson Med, Vol. 41, pp. 939-946, 1999.
[60]T. E. Nichols, and A. P. Holmes. “Nonparametric permutation tests for functional neuroimaging: a primer with examples”, Human Brain Mapping, Vol. 13, pp. 589-600, 2002.
[61]S. Ogawa, T. M. Lee, A. R. Kay, and D. W. Tank. “Brain magnetic resonance imaging with contrast dependent on blood oxygenation”, Proc Natl Acad Sci, USA Vol. 87, pp. 9868–9872, 1990.
[62]S. Ogawa, D. W. Tank, R. Menon, J. M. Ellermann, S. G. Kim, H. Merkle, and K. Ugurbil. “Intrinsic Signal Changes Accompanying Sensory Stimulation: Functional Brain Mapping with Magnetic Resonance Imaging”, Proc. Natl. Acad. Sci, USA Vol. 89, pp. 5951-5955, 1992.
[63]S. Ogawa, T. M. Lee, and B. Barrere. “The Sensitivity of Magnetic Resonance Image Signals of a Rat Brain to Changes in the Cerebral Venous Blood Oxygenation”, Magn. Reson. Med, Vol. 29, pp. 205-210, 1993.
[64]Y. C. Okada. “The hippocampal formation as a source of the slow endogenouspotentail”, Electroenceph clin Neurophysiol, Vol. 55, pp. 417-426, 1983.
[65]N. R. Pal, J. C. Bezdek, and E. C.-K. Tsao. “Generalized Clustering Networks and Kohonen’s Self-Organizing Scheme”, IEEE Trans. Neural Networks, Vol. 4, pp.549-556, 1993.
[66]R. D. Pascual-Marqui, C. M. Michel, and D. Lehmann. “Low resolution electromagnetic tomography : a new method for localizing electrical activity in the brain”, International Journal of Psychophysiology, Vol. 18, pp. 49-65, 1994.
[67]R. D. Pascual-Marqui, “Review of methods for solving the EEG inverse problem”, International Journal of Bioelectromagnetism, Vol. 1, pp. 75-86, 1999.
[68]C. Phillips, M. D. Rugg, and K. J. Friston. “Systematic regularization of linear inverse solutions of the EEG source localization problem”, Neuroimage, Vol. 17, pp. 287-301, 2002.
[69]M. Quigley, V. Haughton, J. Carew, D. Cordes, C. Moritz, K. Arfanakis, and M. E. Meyerand. “Comparison of independent component analysis and conventional hypothesis-driven analysis for clinical functional MR image processing”, AJNR Am. J. Neuradiology, Vol. 23, pp. 49-58, 2002.
[70]T. W. Ridler. “Calvard S. Picture thresholding using an iterative selection method”, IEEE Transactions on Systems, Man, and Cybernectics, Vol. 8, No. 8, pp. 630-632, 1978.
[71]G. Scarth, M. McIntrye, B. Wowk, and R. L. Somorjai. “Detection Novelty in Functional Images Using Fuzzy Clustering”, Proc. SMR 3rd Ann. Meeting, Nice, France, pp. 238, 1995.
[72]G. Scarth, M. Alexander, M. McIntyre, B. Wowk, and R. L. Somorjai. “Artifact Detection in fMRI Using Fuzzy Clustering”, Proc. ISMRM 4th Ann. Meeting, New York, USA, pp. 1783, 1996.
[73]G. Scarth, E. Moser, R. Baumgartner, M. Alexander, and R. L. Somorjai. “Paradigmfree fuzzy clustering-detected activations in fMRI: a case study”, Proc., SMR, 4th Annual Meeting, New York, pp. 1784, 1996.
[74]M. Scherg, and D. Von Cramon. “Evoked dipole source potentials of the humanauditory cortex”, Electroencephalogr Clin Neurophysiol Vol. 65, pp. 344-360, 1986.
[75]M. Scherg, T. Bast, and P. Berg. “Multiple source analysis of intercial spikes: goals, requirements, and clinical value (Review)”, J Clin Neurophysiol, Vol. 16, pp. 214-24, 1999.
[76]N. Sobel, V. Prabhakaran, Z. Zhao, J. E. Demond, G. H. Glover, E. V. Sullivan, and J. D. Gabrieli. “Time course of odorant-induced activation in the human primary olfactory cortex”, J. Neurophysiol. Vol. 83, pp. 537–551, 2000.
[77]J. A. Sorenson and X. Wang. “ROC Methods for Evaluation of fMRI Techniques”, Magn, Reson. Med, Vol. 36 pp. 737-744, 1996.
[78]M. C. Su, N. DeClaris, and T. K. Liu. “Application of neural networks in cluster analysis”, in IEEE Int. Conf. on Systems, Man and Cybernetics, pp. 1-6, Orlando, Oct. 1997.
[79]W. Sukov, and D. S. Barth. “Three-dimensional analysis of spontaneous and thalamically evoked gamma oscillations in auditory cortex”, J. Neurophysiol, Vol. 79, pp. 2875-2884, 1998.
[80]J. J. Sychra, P. A. Bandettini, N. Bhattacharya and Q. Lin. “Synthetic Images by Subspace Transforms I. Principal Components Images and Related Filters”, Med. Phys., Vol. 21, pp. 193-201, 1994.
[81]J. Talairach, and P. Tournoux. “Co-Planar Stereotaxic Atlas of the Human Brain: Three-Dimensional Proportional System”, Georg Thieme, Stuttgart, 1988.
[82]P. Toft, L. K. Hansen, F. A. Nielsen, C. Goutte, S. Strother, N. Lange, N. Morch, C. Svarer, O. B. Paulson, R. Savoy, B. Rosen, E. Rostrup, and P. Born. “On clustering of fMRI time series”, In Third International Conference on Functional Mapping of the Human Brain. NeuroImage Vol.3, No. 3, pp. S456, 1997.
[83]V. L. Towle, J. Bolanos, D. Suarez, K. Tan, R. Grzeszczuk, D. N. Levin, R. Cakmur, S. A. Frank, and J. P. Spire. “The spatial location of EEG electrodes: locating the best-fitting sphere relative to cortical anatomy”, Electroencephalogr Clin Neurophysiol, Vol. 86, pp. 1-6, 1993.
[84]A. Ultsh, and H. P. Siemon. “Kohonen’s self organizing feature maps for exploratory data analysis”, Proceedings of International Neural Networks Conference (INNC’90), pp. 305-308, 1990.
[85]R. E. Walpole, and R. H. Myers. Probability and statistics for engineers and scientists, 4th ed., Macmillan Publishing Co., New York.
[86]B. Whitcher, A. J. Schwarz, H. Barjat, S. C. Smart, R. I. Grundy, and M. F. James. “Wavelet-Based Cluster Analysis: Data-driven Grouping of Voxel Time-Courses with Application to Perfusion-Weighted and Pharmacological MRI of the Rat Brain”, NeuroImage, 2004.
[87]K. Y. Wong, “Multi-function Auto Thresholding Algorithm,” IBM Tech. Disclosure Bull. Vol. 21, No. 7, pp. 3001-3003, 1978.
[88]J. Xiong, J. H. Gai, J. L. Lancaster, and P. T. Fox. “Clustered pixels analysis for functional MRI activation studies of the human brain”, Hum Brain mapping, Vol. 3, pp. 287-301, 1995.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔